An earlier version of this article appeared in People Matters (May 2017). https://www.peoplematters.in/article/technology/a-focus-on-measures-evidence-based-science-is-important-15543

​In the future, we will see talent analytics efforts pivot in three important ways: (a) mature from lending administration support to augmenting performance, (b) transition from describing the past to providing future-focused insights, (c) evolve managerial decision making about people from intuition-led, to one that is more objective and fact-based.

According to a 2013 CEB survey, only 18% of business leaders say they trust their talent data. A staggering 82% don’t believe their talent analytics focus on the right issues, and only 15% of HR leaders say they have made a business decision based on insights gleaned from HR analytics. Not much has changed in 2018 according to a survey conducted by consulting firms, DDI, The Conference Board, and Ernst and Young. Yet, investment in talent analytics globally is upwards of 600 million dollars. How can organizations derive greater value from their people analytics practice?

​In this article, I present the thesis that organizations that avoid some of the common pitfalls associated with talent analytics, adopt an evidence-based focus, and deliver personalized insights will be better positioned to fulfill the promise that the practice has to offer. If done right, it can offer a significant competitive advantage to organizations. Imagine a learning plan that is automatically created based on the future skill needs and aspirations of an employee. How about the intelligent identification of rotational opportunities to grow your future leaders. Imagine automatic rescheduling of tasks for optimal time utilization, and intelligent calendar management to promote work-life balance. The possibilities are tremendous, and the journey for most organizations has just begun.

​Talent analytics is a mechanism to uncover insights from people and business data that might otherwise be invisible through pure observations. To be of utility, these insights must align with the critical priorities of the organization. Better still, they must not just describe the current state but provide a forward-looking view of what’s to come. This is the value of predictive and prescriptive analytics. A review of HR practices shows that the vast majority of the people operations such as hiring, development, and performance management have primarily been intuition-led. This is likely the reason why HR has struggled to earn the credibility of our counterparts in manufacturing, finance, or marketing, who have a long history of data-based decision making. HR has an equal or better opportunity to be analytics led. Ultimately, all HR activities generate data about people – attendance, absence, time spent to more abstract concepts like trust, leadership, and influence. In fact, we have 150 years of quantified science supplying a deep understanding of human behavior in the workplace. However, there has been a limited adoption of this knowledge and understanding. Below I elaborate on some of the most limiting challenges that organizations must overcome.

Many organizations rely on a centralized analytics function. Given the scarcity of qualified data science expertise, this may seem like a prudent choice. However, such an organizational design has many limitations. To conduct timely and meaningful analytics, it is important to spot and capture opportunities, such that they can be viewed from an analytical perspective. It is also beneficial to combine analytics with experimentation. Organizations that have a centralized analytics function tend to engage these teams when there is data available to be analyzed. This is often too late. It offers fewer opportunities to influence the program design and data collection plan. As a result, the analytics are more descriptive than predictive.

The second challenge relates to over-reliance on past company data. For example, organizations spend countless hours mining their employee engagement survey to understand the drivers of engagement. In one such exercise, the data scientists found that of all the practices that were evaluated in their survey, only one had a strong association with employee turnover. In particular, the analyses revealed that those employees who took their vacation days were less likely to exit the organization than those who saved their leave. Based on this finding, the company determined that managers must encourage employees to take all their vacation days within a calendar year. One challenge with this approach could be a false cause-effect attribution. It is possible that those who are saving their vacation days are already planning to leave the organization and perhaps are hoping to catch them out. A better approach would be to look at the predictors of turnover, and select interventions that have a higher probability of delivering the desired outcome. In the case of turnover, a combination of five elements has repeatedly been found in empirical studies to be among the most efficacious predictors. The five factors include satisfaction with pay, promotion, manager, coworker, and the work itself. Basing interventions on prior evidence and designing experiments to find the best solution may be a better use of the analytics functions. The practice of conducting AB testing in marketing leverages a similar idea of experimentation.

The third challenge pertains to the assumption that to perform impactful analytics one requires big data. This is a fallacy and one plaguing not just HR but the broader business world. With increasing digitization, data velocity and variety is no longer a challenge. A learning management system can generate 1000 data points per user with just 10 minutes of interactions. Even when big data is available, the challenge is in finding the signal in the noise. That is, making sense of the data in a way that each stakeholder can take meaningful action. Additionally, to do useful talent analytics, organizations need more than just data (information residing in various systems which can be counted). They must invest in capturing abstract constructs in a quantitative format. These are called measures. A measure provides ruler-like properties to abstract phenomenon (e.g., risk, trust, leadership, making them amenable to sophisticated analyses. For examples, in a learning platform, it is easy to capture transactional data about time spent, and activities attempted, but these may not predict learning and skill development. To measure skill one must measure concepts such as knowledge structures and self-efficacy.

​To overcome some of these challenges, it is essential to evaluate the organizational structure and tighten the integration between the talent analytics team and the HR function. Secondly, the organization must invest in developing specialized talent analytics skills that go beyond statistics, data science, and software programming. Talent analytics team members must have a sound understanding of the science of human performance and evidence-based methods. They must also have expertise in specialized statistical techniques relevant for measuring and analyzing people data such as scaling and hierarchical linear modeling.

​As we look to the future, I see talent analytics efforts pivot in three crucial ways, (a) maturing from lending administration support to augmentation performance through predictive insights, (b) evolving from describing the past to providing future-focused insights, (c) evolving managerial decision making about people from intuition-led, to one that is more objective and fact-based. Next, I elaborate on each of these points.

​Historically, talent analytics have been used to support administrative decisions, for instance, demonstrating program impact and summarizing workforce characteristics (e.g., gender distribution, salary ranges), all primarily designed to support administrative decision making. In the future, we will see greater use of analytics to augment employee performance. The language editing tool, Grammarly, is one such example. It reviews writing habits and provides real-time grammar suggestions and learning tips. Imagine similar tools that help a manager prepare for a motivating performance review meeting, or prepare to negotiate with a difficult client. The potential for such methods in unlocking productivity is remarkable. To provide personalized performance support, the analytics must not just focus on the past data but reliably predict what one is likely to do. This is the benefit of prescriptive analytics which enables one to find the best option across multiple future states. Prediction and prescription are some of the key reasons why focus on measures, and evidence-based science is important. The net result of using analytics that provides performance support and presents a forward-looking view is in creating an equal playing ground where the best can rise to the top. I am personally most excited about the potential of talent analytics in creating fair and equitable workplaces, where meritocracy can thrive.

​In conclusion, I see talent analytics as the third eye and the sixth sense. It can help you see what’s not readily visible, enable better sensing, and power your abilities with greater intelligence. Finally, I leave you with this question, if this future state were to come true, how would your life be different?